Nick Omeyer nomeyer

## create_app_from_pid.m
#include <CoreFoundation/CoreFoundation.h>
AXUIElementRef app = AXUIElementCreateApplication(pid);

## code.ts
const lines = [
  'I am a string',
  'I am not a string',
  'Lies',
  'You got me',
];

function printLines(callback: () => void) {
  lines.forEach((line, i) => console.log(`Line ${i}: ${line}`));
  callback();

## pd_lowercase_col.txt
df['x'].str.lower()

## dict_object_attributes.py
def __iter__(self):
    for attr, value in self.__dict__.iteritems():
        yield (attr, value)

## tree.md

      
              1 file
            
          
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                nomeyer
                / tree.md
            
            
              Created
              May 19, 2016 16:14
                — forked from hrldcpr/tree.md
            
              
                one-line tree in python
              
          
    One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!

  
## printable_groups.py
groups = dict(list(gb))

## pandas_order_by.py
   c1   c2
0   3   10
4   2  100
1   2   30
3   2   15
2   1   20

## fetch_parse_xml.py
from xml.dom.minidom import parse, parseString
import urllib2
# note - i convert it back into xml to pretty print it
print parse(urllib2.urlopen("http://search.twitter.com/search.atom?&q=python")).toprettyxml(encoding="utf-8")

## torch_ubuntu_install.md

      
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                nomeyer
                / torch_ubuntu_install.md
            
            
              Last active
              April 2, 2016 18:26
            
              
                Guide to setting up Torch on an AWS GPU instance (copied from https://github.com/brotchie/torch-ubuntu-gpu-ec2-install)
              
          
    Installing Torch on Ubuntu 14.04 Amazon EC2 GPU Instances

This is a guide for installing the Torch machine learning ecosystem onto a GPU EC2 instance running Ubuntu 14.04.
Note: I have created and made available a Community EC2 AMI following these step with the name torch-ubuntu-14.04-cuda-7.0-28 and ami-id ami-c79b7eac. Simply search for ami-c79b7eac in Community AMIs when creating an instance to get up and running quickly.
Preliminary steps:

Start a g2.2xlarge or g2.8xlarge instance with the Ubuntu Server 14.04 LTS (HVM), SSD Volume Type - ami-d05e75b8 base AMI;
On Step 4: Add Storage of the instance configuration, increase storage on the primary volume from 8GB to 16GB; the 8GB default is too small;
Ensure the SSH port is allowed in the security group;


## log_nsstring_c.m
fprintf(stdout, "%s\n", [str UTF8String]);
fflush(stdout);
	#include <CoreFoundation/CoreFoundation.h>
	AXUIElementRef app = AXUIElementCreateApplication(pid);
	const lines = [
	'I am a string',
	'I am not a string',
	'Lies',
	'You got me',
	];

	function printLines(callback: () => void) {
	lines.forEach((line, i) => console.log(`Line ${i}: ${line}`));
	callback();
	def __iter__(self):
	for attr, value in self.__dict__.iteritems():
	yield (attr, value)
	from xml.dom.minidom import parse, parseString
	import urllib2
	# note - i convert it back into xml to pretty print it
	print parse(urllib2.urlopen("http://search.twitter.com/search.atom?&q=python")).toprettyxml(encoding="utf-8")